Joint Trajectory and Transmit Power Design for Cellular-Connected UAVs via Differentiable Channel Knowledge Map

This paper proposes a mobile unmanned aerial vehicle (UAV) jamming scheme under the probabilistic line-ofsight channel model (PLCM) to improve the secrecy of ground wiretap channels, in which a friendly UAV is deployed to cooperatively transmit jamming signals to confuse the suspicious eavesdropper. Our goal is to maximize the average (expected) secrecy rate by jointly optimizing the source transmit power, UAV jamming power and trajectory for a given flight time. Since the expected secrecy rate is highly complicated with respect to the UAV trajectory, we derive a more tractable lower bound for it. Nevertheless, the resulting optimization problem remains a non-convex problem, which is difficult to solve optimally. Therefore, we propose an efficient iterative algorithm to obtain a suboptimal solution to it by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Simulation results show that the joint power and trajectory optimization scheme under the PLCM significantly outperforms various benchmark schemes.

Mobile relaying is emerged as a promising technique to assist wireless communication, driven by the rapid development of unmanned aerial vehicles (UAVs). In this paper, we study secure transmission in a four-node (source, destination, mobile relay, and eavesdropper) system, wherein we focus on maximizing the secrecy rate via jointly optimizing the relay trajectory and the source/relay transmit power. Nevertheless, due to the coupling of the trajectory designing and the power allocating, the secrecy rate maximization (SRM) problem is intractable to solve. Accordingly, we propose an alternating optimization (AO) approach, wherein the trajectory designing and the power allocating are tackled in an alternating manner. Unfortunately, the trajectory designing is a nonconvex problem, and thus is still hard to solve. To circumvent the nonconvexity, we exploit sequential convex programming (SCP) to derive an iterative algorithm, which is proven to converge to a Karush-Kuhn-Tucker (KKT) point of the trajectory design problem. The simulation results demonstrate the efficacy of the joint power and trajectory design in improving the secrecy throughput.

Joint Trajectory and Power Design with Cooperative Jamming UAV Assistance Based on Reinforcement Learning

In this letter, an unmanned aerial vehicle (UAV) is employed as relay to build temporary communication links for disconnected ground nodes. To improve the fairness and energy efficiency, two scenarios are investigated. First, for given transmission power budget, we consider maximizing the minimum information rate by joint power and trajectory optimization over a time horizon. Then, for given required information rate, we consider minimizing the sum-power consumption. These two objectives are both non-convex, which is intractable to solve. To address these objectives, we propose iterative algorithms to approximate the solution by successive convex optimization techniques. Numerical results show the effectiveness of the proposed approaches.

In this work, we explore a multi-UAV-assisted wireless power transfer (WPT) network, where multiple UAVs are deployed to provide WPT services to multiple ground devices (GDs) in order to extend their lifespan. To enhance the WPT efficiency while considering fairness, we investigate the joint trajectories and transmit power design. For fairness-aware consideration, our objective is to maximize the harvested energy of the GD with the worst condition, taking into account UAV mobility, anti-collision, and power budget constraints. Unlike previous works that focus on the simplified linear energy harvesting (EH) model, a more accurate multi-source nonlinear EH model is, for the first time, adopted to formulate the problem. Given the highly non-convex nature of the original problem due to the presence of coupled variables, we leverage the convexity of the multi-source nonlinear EH model and introduce a convex approximation method, which enables us to construct a tightly convex problem in each iteration for the original joint design problem, thereby obtaining a high-quality solution. Finally, we present numerical results to showcase the convergence of our algorithm and validate the performance advantages of the proposed multi-UAV WPT scheme with a nonlinear EH model versus benchmarks.

Joint trajectory and power design for UAV-enabled cooperative jamming in two-way secure communication

Unmanned aerial vehicles (UAVs) have attracted growing interest in wireless communications due to their several superiorities, such as highly controllable mobility, favorable communication links with the ground, on-demand deployment, and low cost. This paper investigates a UAV-ground communication system, where a UAV is dispatched to send classified information to a legitimate user in the presence of an eavesdropper on the ground while simultaneously avoiding flying over the no-fly zones (NFZs). We aim to maximize the average secrecy rate of the system by jointly optimizing the UAV's trajectory and transmit power over a given flight period under the practical constraints on the UAV's maximum speed, the initial and final locations, avoidance of NFZs, as well as the transmit power. Although the existing works have studied a similar secrecy rate maximization problem, they all rely on general-purpose solvers, which leads to considerably high computational complexity. To address this issue, we propose an efficient algorithm by applying the techniques of alternating optimization (AO) and successive convex approximation (SCA) to obtain a suboptimal solution and utilizing the alternating directional method of multipliers (ADMM) under the SCA framework to realize low-complex implementation. The simulation results demonstrate the superior computational efficiency of our proposed algorithm and show the impact of NFZs on the system.

Joint Trajectory and Power Design for Secure UAV-Enabled Multicasting

In this paper, we study an unmanned aerial vehicle (UAV)-enabled covert communication network in which a UAV communicates to multiple ground users (GUs) without being detected by a ground detector. Considering the fairness, we aim at a joint UAV trajectory and transmit power design to maximize the minimum throughput among all GUs under constraints including mobility and covertness. By characterizing the covertness constraint, the joint design problem is transformed to a pure trajectory design which is still non-convex and with infinite number of variables. To address the problem, we adopt the optimal successive-hover-and-fly (SHF) trajectory structure to reformulate it to a new one with limited number of variables, and efficiently solve the reformulated problem via introducing a set of tight convex approximations to the problem and applying the successive convex approximation (SCA) method. Simulation results show the high performance with respect to covert communication throughput and the low complexity of proposed design in comparison to the benchmark.

The use of unmanned aerial vehicles (UAV) as aerial communication platforms has gained significant attention due to their favorable air-to-ground channels, on-demand deployment, and highly controllable mobility. In this paper, we aim at developing an efficient algorithm for the joint transmit power and UAV trajectory design via maximizing the minimum average throughput in a UAV-enabled network. The conventional method to tackle this problem is to optimize transmit power or trajectory in an alternative manner, which generally incurs high computational complexities. Moreover, how to find a favorable initial point is a challenging task. To this end, we impose a novel constraint by assuming that the UAV will not communicate until it approaches the closest position to a user, thus transforming the initial problem into an approximate one with reduced variables. Based on this result, we further propose two low-complexity algorithms by exploiting the alternating directional method of multipliers (ADMM), whose updating step is performed in closed-form solutions. In the first algorithm, both UAV trajectory and transmit power are determined simultaneously. We also propose a second algorithm which iteratively optimizes transmit power and trajectory. The simulation results show that by using the proposed ADMM-based algorithms, one can achieve higher performance than state-of-the-art methods with reduced computation time.

This correspondence aims to maximize the average throughput via the joint design of the transmit power and trajectory for unmanned aerial vehicle (UAV)-enabled network. The conventional way to tackle this problem is based on the alternating optimization (AO) method by iteratively updating power and trajectory until convergence, resulting in a non-convex trajectory subproblem which is difficult to deal with. To develop more efficient methods, we propose a novel AO method by incorporating both power and trajectory into an intermediate variable, and then iteratively updating power and the newly introduced variable. This novel variable transformation makes it easier to decompose the original problem into two convex subproblems, namely a throughput maximization subproblem and a feasibility subproblem. Consequently, both of these subproblems can be solved in a globally optimal fashion. We further propose a low-complexity algorithm for the feasibility subproblem by exploiting the alternating directional method of multipliers (ADMM), whose updating step is performed in closed-form solutions. Simulation results demonstrate that our proposed method reduces the computation time by orders of magnitude, while achieving higher performance than the conventional methods.

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

In this paper, we investigate secure UAV communications considering the estimation error for eavesdroppers position information and user service fairness requirement. The average worst-case secrecy rate is intentionally maximized by optimizing the UAV’s trajectory and transmit power, and the legitimate ground users scheduling jointly, subject to the UAV’s mobility and transmit power constraints, and the user scheduling and fairness constraints. An iterative algorithm based on the block coordinate descent (BCD) method and successive convex approximation (SCA) technique is proposed. Simulation results show that our proposed algorithm can provide joint trajectory, transmit power and user scheduling design under fairness consideration.

Channel knowledge map (CKM) is an emerging technique to enable environment-aware wireless communications, in which databases with location-specific channel knowledge are used to facilitate or even obviate real-time channel state information acquisition. One fundamental problem for CKM-enabled communication is how to efficiently construct the CKM based on finite measurement data points at limited user locations. Towards this end, this paper proposes a novel map construction method based on the expectation maximization (EM) algorithm, by utilizing the available measurement data, jointly with the expert knowledge of well-established statistic channel models. The key idea is to partition the available data points into different groups, where each group shares the same modelling parameter values to be determined. We show that determining the modelling parameter values can be formulated as a maximum likelihood estimation problem with latent variables, which is then efficiently solved by the classic EM algorithm. Compared to the pure data-driven methods such as the nearest neighbor based interpolation, the proposed method is more efficient since only a small number of modelling parameters need to be determined and stored. Furthermore, the proposed method is extended for constructing a specific type of CKM, namely, the channel gain map (CGM), where closed-form expressions are derived for the E-step and M-step of the EM algorithm. Numerical results are provided to show the effectiveness of the proposed map construction method as compared to the benchmark curve fitting method with one single model.

Cellular-connected unmanned aerial vehicle (UAV), as a promising application of extending cellular service from ground to low-altitude three-dimensional (3D) airspace, has received significant attention recently. However, its practical realization faces some critical challenges, such as the noncontinuous 3D cellular coverage in the sky, as well as the complex physical and radio environment when operating in urban area. In this paper, by exploiting the UAV’s highly controllable mobility, we study the UAV trajectory design problem to minimize the weighted sum of mission completion time and expected communication outage duration, while ensuring obstacle avoidance in complex environment. The formulated problem involves intractable cost function and constraint, which can not be solved by standard optimization techniques. To this end, we first study the performance upper bound based on the Dijkstra’s shortest path algorithm under the ideal assumption that the perfect physical environment information and radio channel knowledge are available. For the practical scenario in the absence of such information, a novel framework with simultaneous environment sensing and channel knowledge mapping is proposed, which aims to construct both the physical environment and radio propagation maps to facilitate the reinforcement learning based path design. Numerical results show that the proposed technique can effectively avoid the coverage holes and physical obstacles, and approaches to the performance upper bound that assumes the perfect physical and radio environment information.